Visual communication systems and applications advocate larger screens and higher resolutions. This tendency is amplified with the appearance of large CRT, LCD, PDP, projector High-Definition (HD) TVs, and digitally processed and stored visual information in the form of MPEG, DVD, DV, etc., in the consumer electronics market. In this era of visual communication, it becomes very important to improve the quality of images and videos that are displayed on large screens at high resolution. Digital TV (DTV) sets often implement the video post-processing functions that improve and enhance the image/video signals to be displayed. The post-processors in these TV sets perform many functions including scaling, noise reduction, detail enhancement, color enhancement, etc., to achieve the goal.
Compression noise reduction, such as MPEG noise reduction, is one of the main functions implemented by the post-processor in a DTV set. Digital video contents may be processed and encoded by a variety of digital compression techniques to overcome the problem with data bandwidth limitation in the communication networks. The current Digital TV (DTV) broadcasting in the U.S. uses the MPEG-2 international video compression standard to compress the digital video contents. The DVD video contents are also processed by MPEG-2. The HD contents may be processed by MPEG-2, MPEG-4, or H.264. These compressed digital videos contain varying degrees of artifacts that deteriorate the quality of displayed video images and scenes. These artifacts in MPEG-processed digital videos are referred to herein as “MPEG noise”, or “compression noise”. The compression noise reduction is, then, a process that detects and removes these annoying MPEG noises from the digital videos before displaying to the screen.
There are different types of MPEG compression noises. One class of MPEG noises includes block artifacts which are appearances of undesired, superfluous edges or discontinuities at the block boundaries. Block artifacts arise in images/videos that are compressed by block-based coding schemes such as JPEG, MPEG, and H.26X. In these coding schemes, a picture is divided into an array of N-by-N rectangular macroblocks (e.g., N is usually 16). Then, each macroblock is again sub-divided into M-by-M (e.g., M is usually 8) sub-blocks. Each sub-block is typically processed by an 8-by-8 Discrete Cosine Transform (DCT), Quantization, Zig-zag scanning, and Entropy coding, independent of other sub-blocks. Because each sub-block (and each macroblock) is processed independently, a critical portion of the image/video data that connects neighboring blocks is often lost and the superfluous edges and discontinuities appear at the block boundaries. Block artifacts become more severe as the image/video is compressed more, i.e., at higher compression rates.
The human visual system (HVS) is extremely efficient at recognizing block artifacts. This is because humans have an extensive amount of visual knowledge and experience about what the world (objects and scenes) looks like. It is very easy, therefore, for humans to detect the artificially generated discontinuities and edges appearing across the picture at a regular interval. Even very small discrepancies are detected without much effort. On the other hand, machines lack the full-extent of visual knowledge that humans have. Specially, simple conventional electronic devices or software programs that are built to detect and remove block artifacts rely only on very restricted inter-pixel, inter-block, or inter-frame relationships. Complete and accurate removal of block artifacts are, therefore, extremely difficult for these machines.